Notice bibliographique
Résumé
Abstract The Problem The energy industry is a mainstay in Alberta's advantageous position as a leader in the Canadian economy. In 1999, energy directly contributed 20.7% (approximately $24 billion) of Alberta's total gross domestic product of $115.4 billion. That figure does not include the significant role the energy industry plays in other sectors, such as manufacturing, transportation, construction, and business. In addition, approximately 40% of the investment that drives Alberta's growth is directly attributable to energy(1). While energy plays a key role in Alberta's economy, we must remember that it is a non-renewable resource. In addition, primary production and existing enhanced oil recovery techniques are only capable of recovering a small percentage of our oil reserves. Current estimates by the Alberta Energy and Utilities Board (AEUB) show that there are 44 billion barrels of oil (72% of the original oil in place) and 78 tcf of natural gas (31% of the initial gas in place) in existing reservoirs, which will remain in the ground unless new and innovative technologies are employed. Although innovation is the key to recovering these reserves, it must also be remembered that the window of opportunity to improve recovery through the use of new technology is limited. The average reserve life index for Alberta oil and gas pools is less than ten years. Once these pools reach the end of their economic life and are abandoned, the opportunity to use existing infrastructure and assets, together with new technology, will likely be lost. The Challenge If Alberta is to maintain current levels of oil and gas revenues, timely development of these innovative technologies is a critical priority. While significant research may be carried out by sectors such as government and universities, industrial R &D, particularly by suppliers, is most clearly linked to technology innovation and, hence, to economic growth. However, over the past decade, both the Alberta government and the oil and gas industry have significantly reduced their oil and gas research and development spending to levels well below those seen in other sectors (see Figure 1). Total Canadian oil and gas industry R&D spending is relatively low for a number of reasons. First, the energy industry is cyclical in nature, leading to limited funding available in the troughs, and limited human resources available during peak times. Larger companies, which formerly maintained their own research departments, now tend to look at research as a cost to be reduced, since investors in public companies demand quarterly growth, and neglect long-term research investment. Instead, industry expects technological solutions to be delivered when needed, mostly by suppliers and service companies. In today's economic climate, industry alone cannot bear all of the risks and the costs inherent in research and development. The Opportunity Industry needs to support the development of new technology to solve problems and unlock opportunities that are unique to Alberta. They also need to share the cost, risk, and the revenue reward of technology development with the Alberta government.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».